Abstract
Accurate prediction of water quality indicators plays an important role in the effective management of water resources. The models which studied limited water quality indicators in natural rivers may give inadequate guidance for managing a canal being used for water diversion. In this study, a hybrid structure (WA-PSO-SVR) based on wavelet analysis (WA) coupled with support vector regression (SVR) and particle swarm optimization (PSO) algorithms was developed to model three water quality indicators, chemical oxygen demand determined by KMnO4 (CODMn), ammonia nitrogen (NH3-N), and dissolved oxygen (DO), in water from the Grand Canal from Beijing to Hangzhou. Modeling was independently conducted over daily and monthly time scales. The results demonstrated that the hybrid WA-PSO-SVR model was able to effectively predict non-linear stationary and non-stationary time series and outperformed two other models (PSO-SVR and a standalone SVR), especially for extreme values prediction. Daily predictions were more accurate than monthly predictions, indicating that the hybrid model was more suitable for short-term predictions in this case. It also demonstrated that using the autocorrelation and partial autocorrelation of time series enabled the construction of appropriate models for water quality prediction. The results contribute to water quality monitoring and better management for water diversion.
Highlights
Due to intensified human activities and growth in living standards, many cities around the world are facing challenges of critically deteriorating water quality [1,2]
Three water quality indicators were modeled, including chemical oxygen demand determined by KMnO4 (CODMn ), ammonia nitrogen (NH3 -N), and dissolved oxygen (DO)
Besides the similar studies that have been done for DO in the river and pond or the turbidity and salinity of water [7,8,29], this study showed that the hybrid structure could be applied in more fields
Summary
Due to intensified human activities and growth in living standards, many cities around the world are facing challenges of critically deteriorating water quality [1,2]. Water quality is a description of the chemical, physical, and biological characteristics of water with respect to its suitability for intended uses [3,4]. Reliable forecasting of water quality allows for the identification of future contaminant problems, and/or the initiation of effective countermeasures to prevent water pollution and protect public health. In China, the South-to-North Water Diversion Project is a major undertaking designed to resolve water shortage problems in northern China. The east route of the project uses an old artificial canal, known as the Grand Canal, which extends from Beijing to Hangzhou, as a diversion structure. The water quality of the Grand Canal is a critical problem for the water diversion project, and predicting the degree of pollution along the canal is essential to guarantee water quality safety
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